28 citations · 52 across the 17 of their papers we have counts for
18 papers
Fine-Tuned Machine-Learned Interatomic Potentials for Structural and Vibrational Properties of Twisted 2D Materials
Viet-Anh Tran, Viet-Hung Nguyen, Wei Chen +2
Twisted van der Waals bilayers form moiré superlattices whose structural and vibrational properties are highly sensitive to variations in local stacking registry and the degree of…
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
Anand Babu, Rogério Almeida Gouvêa, Gian-Marco Rignanese
Inverse materials design is shifting materials discovery from forward prediction toward targeted proposal of candidates that satisfy objectives under physical constraints. Here, we…
VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
Rogério Almeida Gouvêa, Gian-Marco Rignanese
While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is…
optimade-maker: Automated generation of interoperable materials APIs from static datasets
Kristjan Eimre, Matthew L. Evans, Bud Macaulay +5
Atomistic structural data are central to materials science, condensed matter physics, and chemistry, and are increasingly digitised across diverse repositories and databases. Inter…
Machine Learning for Electrode Materials: Property Prediction via Composition
Hao Wu, Cameron Hargreaves, Arpit Mishra +1
In this work, we benchmark three leading composition based Machine Learning (ML) frameworks, MODNet, CrabNet, and a random forest model based on Magpie features, predicting the pro…
A critical assessment of bonding descriptors for predicting materials properties
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen +4
Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuabl…
From Symmetry to Stability: Structural and Electronic Transformation in CsKInI
Mohammad Bakhsh, Victor Trinquet, Rogério Almeida Gouvêa +2
CsKInI is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations…
MEIDNet: Multimodal generative AI framework for inverse materials design
Anand Babu, Rogério Almeida Gouvêa, Pierre Vandergheynst +1
In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastiv…
On the Role of Interlayer Electrons on the Frictional Behavior of Two-Dimensional Electrides
Jingcheng Qi, Giuliana Materzanini, Gian-Marco Rignanese +2
Friction accounts for up to 30% of global energy consumption, underscoring the urgent need for superlubricity in advanced materials. Two-dimensional (2D) electrides are layered mat…
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
Rogério Almeida Gouvêa, Pierre-Paul De Breuck, Tatiane Pretto +2
This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent…
Generative AI for Crystal Structures: A Review
Pierre-Paul De Breuck, Hai-Chen Wang, Gian-Marco Rignanese +2
As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases…
Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials
Matthieu J. Verstraete, Joao Abreu, Guillaume E. Allemand +71
Abinit is a widely used scientific software package implementing density functional theory and many related functionalities for excited states and response properties. This paper p…
Lithium Intercalation in the Anisotropic van der Waals Magnetic Semiconductor CrSBr
Kseniia Mosina, Aljoscha Söll, Jiri Sturala +9
Alkali metal intercalation is an important strategy for doping van der Waals materials. Lithium, in particular, was shown to achieve exceptional charge carrier densities, reaching…
Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening
Victor Trinquet, Matthew L. Evans, Gian-Marco Rignanese
Due to their abundant use in all-solid-state lasers, nonlinear optical (NLO) crystals are needed for many applications across diverse fields such as medicine and communication. How…
Machine Learning on Multiple Topological Materials Datasets
Yuqing He, Pierre-Paul De Breuck, Hongming Weng +2
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Da…
A generative material transformer using Wyckoff representation
Pierre-Paul De Breuck, Hashim A. Piracha, Gian-Marco Rignanese +1
Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite rece…
Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences
Matthew L. Evans, Gian-Marco Rignanese, David Elbert +1
Two key issues hindering the transition towards FAIR data science are the poor discoverability and inconsistent instructions for the use of data extractor tools, i.e., how we go fr…
Optical materials discovery and design with federated databases and machine learning
Victor Trinquet, Matthew L. Evans, Cameron J. Hargreaves +2
Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are inc…